Advancing stroke rehabilitation: the role of wearable technology according to research experts
Bibliographic record
Abstract
BACKGROUND: Advancements in wearable technology have created new opportunities to monitor stroke survivors' behaviors in daily activities. Research insights are needed to guide its adoption in clinical practice, address current gaps, and shape the future of stroke rehabilitation. This project aims to: (1) Understand stroke rehabilitation researchers' perspectives on the opportunities, challenges, and clinical relevance of wearable technology for stroke rehabilitation, and (2) Identify necessary next steps to integrate wearable technology in research and clinical practice. METHODS: Using a phenomenological qualitative design, two 90-minute focus groups were conducted with 12 rehabilitation researchers. The focus groups consisted of semi-structured, open-ended questions on functional movement behavior, motor performance and benefits and pitfalls of wearable technology. The transcribed focus groups were analyzed using inductive thematic analysis. RESULTS: Three main themes were derived from the analysis: (1) Assessing activity performance is critical to inform interventions, (2) The demonstrated benefit is not commensurate with the added hassle, (3) Collaboration is needed between the industry, academia and end-users. Necessary future steps were recognized including the identification of intuitive and actionable metrics, and the integration of sensor-derived data with electronic health records and into clinical workflow to support self-management strategies. CONCLUSION: Wearable technology shows great potential to complement and support stroke rehabilitation. Many key barriers to clinical adoption remain which underscore the necessity to foster collaborations between industry, academia, and the participants we serve.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.165 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".